code: def _add_undefined_nodes_info(
      causal_nodes_info: dict, present_nodes: list
  ) -> None:
      for present_node in present_nodes:
          if present_node not in causal_nodes_info:
              logger.warning(
                  f"{present_node} is undefined and will be considered as root node by default."
              )
              causal_nodes_info[present_node] = {}
              causal_nodes_info[present_node][
                  "causal_mechanism"
              ] = EmpiricalDistribution()
              causal_nodes_info[present_node]["fully_defined"] = False
  
imports: import ast
  import logging
  import re
  from typing import Tuple
  import networkx
  import numpy
  import scipy.stats
  from dowhy.gcm import AdditiveNoiseModel, EmpiricalDistribution, ScipyDistribution, StructuralCausalModel
  from dowhy.gcm.causal_mechanisms import StochasticModel
  from dowhy.gcm.causal_models import PARENTS_DURING_FIT
  from dowhy.gcm.ml.prediction_model import PredictionModel
  from dowhy.gcm.util.general import shape_into_2d
  from dowhy.graph import get_ordered_predecessors
label_desc: Use image node_id 8 for calling a global function with example usage: _add_undefined_nodes_info(causal_nodes_info, present_nodes) without return types